This paper presents a principal component (PC) analysis based method for online characterization of outliers in synchrophasor measurements. To that end, a linearized framework is established to analyze dynamical response from a system under nominal and off-nominal (e.g., faulted) conditions, which are contained in the same window of synchrophasor data. Inspired by the singular value perturbation theory, a bound on the change in the norm of the PC scores as a function of system state matrices is presented. It is shown that in the presence of bad data outliers these bounds for higher dimensional PC scores will be significantly larger compared to lower dimensions. The effect of the number of samples in the data window on the results of the analysis is established. Case studies on a simulated test system and on field data collected from a US utility are presented to support the analytical results. Finally, an online classifier for the characterization of outliers is developed to illustrate the usefulness of the proposed framework for machine learning based methods.
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering